课程: Complete Guide to AI and Data Science for SQL: From Beginner to Advanced

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Model performance comparison on train and test data

Model performance comparison on train and test data - SQL教程

课程: Complete Guide to AI and Data Science for SQL: From Beginner to Advanced

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Model performance comparison on train and test data

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- [Instructor] Welcome to step 19, where you assess how well your model performs on both the training and test data sets. Now, why are you doing this? Think of it this way. After building a high performance sports car, you'd want to test it out on various terrains and conditions to make sure it's running smoothly. Similarly, you want to evaluate your model's performance in different scenarios to ensure it's reliable. In this step, you'll use three important metrics to gauge your model's performance. Let's introduce these metrics before diving into the results. First, the RMSE, or root mean square error. This is similar to calculating the typical gap between your predicted home values and their actual value. In other words, it tells you how far off, on average, your predictions are from real home values. A lower RMSE is better, because it means your model's predictions are closer to reality. MAE, or mean absolute error.…

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